Won Group
The goal of the laboratory is to develop computational tools to unravel biological mechanisms and enhance our understanding of biological systems integrative data analysis. We use various approaches including machine learning, artificial intelligence, and image processing technologies to study cell interactions, fate decision, cancer progression and precision medicine.
We study dynamic behavior of cells in association with their environment
We study the function of a cell in harmony with its environment along the development. Besides classical omics data including epigenomic, proteomic, transcriptomic and genomic data from single as well as bulk cells, we integrate various information such as cell size, neighboring cells, tissue architectures to understand the local and global environment.
- TENET is to identify gene regulatory rules from scRNAseq (Nucleic Acid Research, 2020). TENET has a power to predict key regulators of the biological processes by applying transfer entropy to detect potential causal relationships between genes. We found Nme2 as a new factor for pluripotency of embryonic stem cells.
More info about TENET
- SHARP performs single cell RNAseq clustering more precisely and rapidly (Genome Research, 2020). By applying random projection, SHARP performs dimension reduction with almost no time cost while largely preserving cell-to-cell distance. SHARP can run even 10 million single cells.
More info about SHARP
- VeTra identifies group of cells that belong to the same developmental trajectory using RNA velocity. Combining with TENET, VeTra suggests key regulators of the developmental path.
More info about VeTra
- CellBIC is an algorithm to perform top-down clustering of single cell RNAseq (Nucleic Acid Research, 2018).
More info about CellBIC
- Defiant is to detect differentially methylated regions.
More info about defiant
- Unraveling gene regulatory mechanisms. “What are the transcriptional target genes and their regulators?” is one of the frequently asked questions in gene regulation to understand cellular processes. To answer this, we use computational approach to understand gene regulatory rules. We developed TENET and further improving it to understand gene regulatory rules. Especially, we try to understand gene regulation driven by external cues.
- Studying cell-interaction. We understand cell interaction and its influences in gene regulation. We apply machine learning algorithms to various datasets including spatial transcriptomics data. We study cell interaction dependent gene regulation and pathway for cell development, cancer progression and brain cell interactions.
- Spatial transcriptomics. Spatial transcriptomics data will determine the location of cells in a tissue with the accompanying transcriptomics information. For this, we apply image processing techniques to perform tissue segmentation and identify differentially expressed genes spatially.
- Drug screening. We develop algorithm for clustering using high throughput screening data. It removes intrinsic biases in a high throughput machine and visualize the similarity and the differences of the multi-dimensional data from hundreds of samples.
- Understanding transcriptional mechanisms. We use how genomic regions are coordinated for gene regulation using epigenomic data. We integrate diverse functional genomics datasets to decipher how genes are transcriptionally and post-transcriptionally regulated.
Selected publications
Kim J, Jakobsen S, Natarajan K,Won KJ*(2020) Gene network reconstruction using single cell transcriptomic data reveals key factors for embryonic stem cell differentiation, Nucleic Acid Research
WanS, Kim J, Won KJ* (2020) SHARP: Single-cell RNA-seq hyper-fast and accurate processing via random projection, Genome Research, 30(2):205-213
Kim YH, Marhon SA, Zhang Y, Steger DJ, Won KJ*, Lazar M* (2018) Rev-erbaDynamically Modulates Chromatin Looping to Control Circadian Gene Transcription, Science, 16;359(6381):1274-1277.
Condon ED, Tran PV, Lien YC, Schug J, Georgieff M, Simmons RA, Won KJ (2018) Defiant: (DMRs: Easy, Fast, Identification and ANnoTation) Identifies Differentially Methylated Regions from Iron-Deficient Rat Hippocampus, BMC Bioinformatics 19(1):31,
Shin HJ, Kim H, Oh S, Lee JG, Kee M, Ko HJ, Kweon MN, Won KJ, Baek SH. (2016) AMPK-SKP2-CARM1 signaling cascade in transcriptional regulation of autophagy. Nature. 2016 Jun 15;534(7608):553-7
Harms MJ, Lim HW, Ho Y, Ishibashi J, Rajakumari S, Steger DJ, Lazar MA, Won KJ*, Seale P* (2015), Prdm16 controls chromatin architecture to determine a brown fat transcriptional program, Gene and Development, 29(3), 298-307. (Co-corresponding).
Step SE, Lim HW, Marinis JM, Prokesch A, Steger DJ, You SH, Won KJ, Lazar MA (2014), Antidiabetic rosiglitazone remodels the adipocyte transcriptome by redistributing transcription to PPARγ-driven enhancers (2014) Genes & Development, 28:9, 1018-1028
Won KJ, Zhang X, Wang T, Raha D, Snyder M, Ren B, Wang W. (2013). Comparative annotation of functional regions in the human genome using epigenomic data, Nucleic Acid Research, 41(8), 4423-4432. PMID: 23482391.
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National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), R01 |
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Novo Nordisk Foundation Under the Program for Translational Hematology |
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Project grant from the Independent Research Fund, Denmark |
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International Network Programme |
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Ascending Investigator Lundbeck Experiment |
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Horizon 2020 – European Commission |